{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis and Forecast of US Inflation\n",
    "\n",
    "**We examine inflation data: CPI and PCE, including the core versions, \n",
    "along with the 10-year BEI rate (break-even inflation). \n",
    "An unified inflation statistic *m4infl* is defined, \n",
    "and we make a univariate forecast using the Holt-Winters \n",
    "model with optimized robust parameters.\n",
    "The stochastic characteristics of *m4infl* are\n",
    "discussed, including its geometric mean rate.**\n",
    "\n",
    "The Fed would like the inflation rate for the US economy \n",
    "to be around 2%. As of 2018-03-07, our one-year forecast \n",
    "is 2.33% -- which calls for moderately tight interest rate policy \n",
    "given current inflation levels.\n",
    "\n",
    "Appendix 2 presents function `foreinfl()` which concisely \n",
    "implements the forecasting process derived in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shortcut to the latest version of this notebook: https://git.io/infl\n",
    "\n",
    "*Dependencies:*\n",
    "\n",
    "- fecon235 repository, https://github.com/rsvp/fecon235\n",
    "- FRED, free data service from the Federal Reserve Bank \n",
    "\n",
    "*CHANGE LOG*\n",
    "\n",
    "    2018-03-12  Appendix 2: Add foreinfl() to distill forecasting.\n",
    "    2018-03-07  Fix #2, update data, optimize Holt-Winters forecast.\n",
    "                    Drop section on correlation to gold.\n",
    "    2015-02-02  Code review and revision. Include sample forecast.\n",
    "    2014-09-02  Introduce new inflation series m4infl. Use getfred().\n",
    "    2014-08-06  Major revision directly based on refined modules.\n",
    "    2014-07-25  Expand to include inflation statistics including BEI.\n",
    "                    Major fix of yi_fred.getdataframe()\n",
    "    2014-07-24  First version includes module yi_fred converted from fred-plot notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fecon235.fecon235 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  Python 2.7.14\n",
      " ::  IPython 5.4.1\n",
      " ::  jupyter_core 4.4.0\n",
      " ::  notebook 5.4.0\n",
      " ::  matplotlib 1.5.1\n",
      " ::  numpy 1.14.1\n",
      " ::  scipy 1.0.0\n",
      " ::  sympy 1.1.1\n",
      " ::  pandas 0.22.0\n",
      " ::  pandas_datareader 0.6.0\n",
      " ::  Repository: fecon235 v5.17.0722 develop\n",
      " ::  Timestamp: 2018-03-12T16:18:51Z\n",
      " ::  $pwd: /media/yaya/virt18g/virt/dbx/Dropbox/ipy/fecon235/nb\n"
     ]
    }
   ],
   "source": [
    "#  PREAMBLE-p6.15.1223d :: Settings and system details\n",
    "from __future__ import absolute_import, print_function, division\n",
    "system.specs()\n",
    "pwd = system.getpwd()   # present working directory as variable.\n",
    "print(\" ::  $pwd:\", pwd)\n",
    "#  If a module is modified, automatically reload it:\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "#       Use 0 to disable this feature.\n",
    "\n",
    "#  Notebook DISPLAY options:\n",
    "#      Represent pandas DataFrames as text; not HTML representation:\n",
    "import pandas as pd\n",
    "pd.set_option( 'display.notebook_repr_html', False )\n",
    "from IPython.display import HTML # useful for snippets\n",
    "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
    "from IPython.display import Image \n",
    "#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n",
    "from IPython.display import YouTubeVideo\n",
    "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n",
    "from IPython.core import page\n",
    "get_ipython().set_hook('show_in_pager', page.as_hook(page.display_page), 0)\n",
    "#  Or equivalently in config file: \"InteractiveShell.display_page = True\", \n",
    "#  which will display results in secondary notebook pager frame in a cell.\n",
    "\n",
    "#  Generate PLOTS inside notebook, \"inline\" generates static png:\n",
    "%matplotlib inline   \n",
    "#          \"notebook\" argument allows interactive zoom and resize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieving data\n",
    "\n",
    "Each economic time series has its own *\"fredcode\"* which is listed \n",
    "at the FRED site, see \n",
    "[yi_fred module](https://github.com/rsvp/fecon235/blob/master/lib/yi_fred.py) \n",
    "for details.\n",
    "\n",
    "Let's create a dictionary `ts` of time series in pandas DataFrame \n",
    "format from our codelists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CPIAUCSL', 'CPILFESL', 'PCEPI', 'PCEPILFE']\n",
      "['GS10', 'FII10', 'm4spx']\n"
     ]
    }
   ],
   "source": [
    "print(ml_infl)\n",
    "print(ml_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  S&P 500 prepend successfully goes back to 1957.\n"
     ]
    }
   ],
   "source": [
    "#  Download updated inflation and bond statistics:\n",
    "ts = {}\n",
    "for i in ml_infl + ml_long:\n",
    "    ts[i] = get(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Collection of historical inflation levels:\n",
    "tsinf = [ ts[i] for i in ml_infl ]\n",
    "\n",
    "#  Then make a DataFrame consisting of those levels:\n",
    "inf_levels = paste( tsinf )\n",
    "#  ... paste() does a side-by-side mash-up\n",
    "#  of individual time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Label the column names:\n",
    "inf_levels.columns = ['CPI', 'CPIc', 'PCE', 'PCEc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Compute year-over-year inflation rates, expressed in percent:\n",
    "inf = pcent(inf_levels, 12).dropna()\n",
    "#  since data is monthly ^"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xac48c22c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  Finished: boxplot-Inflation.png\n"
     ]
    }
   ],
   "source": [
    "boxplot( inf, 'Inflation' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The small appended \"c\" denotes *core* version of headline versions of inflation. \n",
    "The red dot represents the most recent data point.**\n",
    "\n",
    "Our inflation rates go back to **1960-01-01**. \n",
    "CPI looks slightly more volatile than PCE versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re: Consumer Price Index and Personal Consumption Expenditures\n",
    "\n",
    "> *\"Two different price indexes are popular for measuring inflation: the consumer price index (CPI) from the Bureau of Labor Statistics and the personal consumption expenditures price index (PCE) from the Bureau of Economic Analysis. [A]n accurate measure of inflation is important for both the U.S. federal government and the Federal Reserve's **Federal Open Market Committee** (FOMC), but they focus on different measures. For example, the federal government uses the CPI to make inflation adjustments to certain kinds of benefits, such as Social Security. In contrast, the **FOMC focuses on PCE inflation in its quarterly economic projections and also states its longer-run inflation goal in terms of headline PCE**. The FOMC focused on CPI inflation prior to 2000 but, after extensive analysis, changed to PCE inflation for three main reasons: The expenditure weights in the PCE can change as people substitute away from some goods and services toward others, the PCE includes more comprehensive coverage of goods and services, and historical PCE data can be revised (more than for seasonal factors only).\"* --James Bullard, president of the Federal Reserve Bank of St. Louis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              CPI        CPIc         PCE        PCEc\n",
      "count  697.000000  697.000000  697.000000  697.000000\n",
      "mean     3.775549    3.757137    3.313203    3.263301\n",
      "std      2.860426    2.590589    2.455981    2.170832\n",
      "min     -1.958761    0.602718   -1.183584    0.947246\n",
      "25%      1.796020    1.963907    1.655400    1.610806\n",
      "50%      3.038138    2.803738    2.545597    2.271314\n",
      "75%      4.647160    4.732510    4.242936    4.358226\n",
      "max     14.592275   13.604488   11.577525   10.233897\n",
      "\n",
      " ::  Index on min:\n",
      "CPI    2009-07-01\n",
      "CPIc   2010-10-01\n",
      "PCE    2009-07-01\n",
      "PCEc   2010-12-01\n",
      "dtype: datetime64[ns]\n",
      "\n",
      " ::  Index on max:\n",
      "CPI    1980-03-01\n",
      "CPIc   1980-06-01\n",
      "PCE    1974-10-01\n",
      "PCEc   1975-02-01\n",
      "dtype: datetime64[ns]\n",
      "\n",
      " ::  Head:\n",
      "                 CPI      CPIc       PCE      PCEc\n",
      "T                                                 \n",
      "1960-01-01  1.240951  2.006689  1.670171  2.028755\n",
      "1960-02-01  1.413793  2.341137  1.686311  2.140951\n",
      "1960-03-01  1.518813  2.000000  1.679398  2.047995\n",
      " ::  Tail:\n",
      "                 CPI      CPIc       PCE      PCEc\n",
      "T                                                 \n",
      "2017-11-01  2.201742  1.712094  1.742919  1.505658\n",
      "2017-12-01  2.111342  1.760828  1.705745  1.526629\n",
      "2018-01-01  2.137869  1.845520  1.650447  1.515219\n",
      "\n",
      " ::  Correlation matrix:\n",
      "           CPI      CPIc       PCE      PCEc\n",
      "CPI   1.000000  0.928298  0.981371  0.913880\n",
      "CPIc  0.928298  1.000000  0.924142  0.964424\n",
      "PCE   0.981371  0.924142  1.000000  0.954527\n",
      "PCEc  0.913880  0.964424  0.954527  1.000000\n"
     ]
    }
   ],
   "source": [
    "stats(inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unified inflation\n",
    "\n",
    "The numbers confirm the core version is less volatile than headline inflation. \n",
    "Moreover, headline versions are most correlated. \n",
    "Note how the dates of the minimum and maximum values do not coincide.\n",
    "\n",
    "So what is the appropriate inflation rate among the contenders? \n",
    "We shall take the *average of the contenders* to arrive at **unified inflation**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Compute unified inflation:\n",
    "inf_av = todf(( inf['CPI'] + inf['CPIc'] + inf['PCE'] + inf['PCEc'] ) / 4 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Y\n",
      "count  697.000000\n",
      "mean     3.527297\n",
      "std      2.466253\n",
      "min     -0.160953\n",
      "25%      1.720500\n",
      "50%      2.687746\n",
      "75%      4.481560\n",
      "max     12.016273\n",
      "\n",
      " ::  Index on min:\n",
      "Y   2009-07-01\n",
      "dtype: datetime64[ns]\n",
      "\n",
      " ::  Index on max:\n",
      "Y   1980-03-01\n",
      "dtype: datetime64[ns]\n",
      "\n",
      " ::  Head:\n",
      "                   Y\n",
      "T                   \n",
      "1960-01-01  1.736641\n",
      "1960-02-01  1.895548\n",
      "1960-03-01  1.811551\n",
      " ::  Tail:\n",
      "                   Y\n",
      "T                   \n",
      "2017-11-01  1.790604\n",
      "2017-12-01  1.776136\n",
      "2018-01-01  1.787264\n",
      "\n",
      " ::  Correlation matrix:\n",
      "     Y\n",
      "Y  1.0\n"
     ]
    }
   ],
   "source": [
    "stats( inf_av )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Speaking of unified inflation rates, we can now say \n",
    "that the maximum occurred in March 1980 at 12%, \n",
    "and that minimum occurred in July 2009 during the \n",
    "Great Recession at -0.16% (slight deflation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  The shortest of our time series under consideration, m4tips10, \n",
    "#  starts at 2003-01-01, so let\n",
    "start = '2003-01-01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LhyVL4KijYMst4b33sj9/4kQz/LFstRWsWuWNvnQsWgTdupmff/JkmDTJNCUboE3X43c+fofDPzwx/CLSTEQmA4uBMao6Ia5Id2BezPaCyD7fqK6u9rO5tCxZYpOZjjoq+QBpKs2JDH+7dv4taBI1/FE6dYI996xm/PimZdevtx7/9tsnr69Yhj9o90U6wqYXnOYg4lWPf3PE1bM90F9E9vCi3lJF1Vw9XbqYMUwX1hnPpk3w+ee2gEksxejxx3L44TBmTNOyb79tWrfaKnl9rsfvcPhHCy8rU9WVIjIOOBKoizm0AIiNN9k+si8hI0aMoDIS6lJRUUFVVdVPT+Co7y3b7ei+XM/3cnv1athii2ratoVly2r4/HOApuXjtUeP19dD167VVFQ0Lt+uHXzwgdVf6N+zcGE13bo1Pt65cw0PPQSDBzcuf+edcPzxqetr376aFSv8//8YOXKkJ/eX05t8u7a2lksvvTQwejLZju4Lip5E+swW1JMTqprXB+gMdIh8bwO8BwyNKzMUeC3yfQDwSYr6tBCMGzeuIPXmwsyZqjvtZN8/+ki1X7/E5ZJpfvRR1ZNOarr/mGNUX37ZG43p6NpVdd68xvveemuctmununx5w74NG1Q7d1atr09d3803q/7+997rTEeQ7otMCJteVafZDyJ2M2O77UWPvxvwqIg0w1xHT6vq6yJyXkTMfZHtoSIyG1gDnO1Bu1kRfWIGgSVLzM0DNuCZzNWTTHMi/z6Yj98PV8+mTfDtt00Trg0ZUs3AgfDEE/b7+vWD//wHdtjBPqno0AFmzy6c5mQE6b7IhLDpBac5iORt+FV1CrBvgv33xm1flG9bpUJ0YBcsFn/pUjOmzZtndv6HH8LIkU33b7WVP4O7S5faHIQttmh67Pjj4aabbDnF886zCWUnnpi+Tufjdzj8o2xSNsT6xorN0qUNhr9lS5vRunRp4zLr1yfWvGoVTJ8OBxzQtF6/evyJBnbB9J5/vmXifP11mDDB3kxOOy19ncWawBWk+yITwqYXnOYgUjaGP0jEunrA3D0LFzZsb9pkPeXly5ue+/HHFiHTqlXTY371+JMZ/nh69YIHH0zv5gHX43c4/KRsDH+QfHaxPX6wGa2xfv45c+Cbb6BZs+om5773HhxySOJ6/QrnTGb487nGxZq5G6T7IhPCphec5iBSNoY/SMT6+KHpAG9dJBD2o4+anvv++5YcLRF+TeDKtMefDa7H73D4R9kY/iD57NK5eqZNg732gjfeqGl03vr1FtEzcGDiev3q8S9cmNzHnyvFMvxBui8yIWx6wWkOImVj+INEOldPXR2cfTbMnNk41/6ECbDrrjYQmohiD+7mQ7t2sHatf9lFHY5ypmwMf5B8dpm4egYMgN69q6mtbdj//vvJ/ftQ/MHdfK5xs2Zm/P328wfpvsiEsOkFpzmIlI3hDwo//GCfDh0a9sW6elQtXHP33c2lE5vALZV/H/zt8adKsZwrxRrgdTjKjbIx/F747FThwgvN154r0eRsErNWTqyrZ948c+VUVEDHjjU/DfBu2mQTtw4+OHndfvT4Ve2NpWvXpsfyvcbF8POHzZcbNr3gNAeRsjH8XjB/vq109fLLudcRP7ALltJ47Vpbwaquznr7YLNfP/zQjO0XX9gDIv7cWPzo8X/3na0h0Lq193W7VbgcDn8oG8Pvhc+urs4mTj3wQO51LF7cNMeNiBn1hQstomePSFLr00+vZsstzd2Tzs0D/vT4Uw3s5nuNi9HjD5svN2x6wWkOImVj+L3gyy/hjDMspHLu3NzqmDUr8YLi221nbp66ugbDLwIjRtjShu+9l97wR+P4C7lWfSEieqK4WH6Hwx/KxvB74bOrq7PcMyefDA8/nFsdM2bAbrs13X/ssZbg7IUXGlw9NTU1nHEGPPts6hm7UVq0sMRpP/yQm7ZMSBbDD/lfYz8XkokSNl9u2PSC0xxEysbwe0G0N3722fDkk02Pf/21JSf74APYuDFxHTNmWCx+PFdcYatqXXmlpTOO0r27JWRr3TqznDeFNp6F7PG3aVPYh5bD4TBEC+kXyAER0aBpAnOfdOxo+eUt4sbcPR07NpTZbz8b+Jw5E+6/33rx8XTtCp99lnr92XjeesseJtdfn75sr162/OFOO2VefzZccgnsuCNcdpn3df/xj3Zt//hH7+sOC+PHQ9u2NnPb4cgUEUFVJX1Jw/X4M2ThQhvY7dTJ8ubvs48Z8Ch1ddYbHjcOfvvbxHl2VqyANWusF58NP/95ZkYfCj/AW6gYfrC3mnXrClN3GPjiC/u/Puss2Ly52GocpUzehl9EtheRsSLypYhMEZFLEpQZJCLLRWRS5HN1vu1mS74+u9hBVzD3S6zhf+IJOPVUeygMHJjY8M+YAbvs0jiG32vNhQ7pTOXqyfcat27tv6snKL7cRYvgmGPgnntsFvOzzyYuFxS92eA0Bw8vll7cCPy3qtaKSDtgooiMVtXpceXeU9VhHrRXFOIN//77wzPP2PfNm83wv/iibffvb5E/GzY0XqVq+vTE/n0v8aPHX0gf/6JFhak76Nx5JwwfboEDnTrBRReZq7B168w7Cg5HpuTd41fVxapaG/m+GpgGJHJmFPX2zTcu98svk/f4P/jAetp7723bHTpAZaW9useSbGA3GbloLmSPX7WwcfzFcPUEJV573Dgz/ACHH273Wvv29jD85JOGckHRmw1Oc/Dw1McvIpVAFTA+weGBIlIrIq+JyB4JjgeaujqbSRulVy/rWS9ZArfcYn792J5ZfJ4dSB7K6SXZ9Pi/+gqOPjpzf/LKlebKatcud32pKIarJwisXm0RXQceaNsiFta7YQPcfDPcfntx9TlKDy9cPQBE3DzPAr+L9PxjmQj0VNW1InIU8CKwS7K6RowYQWVlJQAVFRVUVVX99ASO+t6y3Y7uy/X8GTOq2XXXxsf33x+uuKKG8ePh2Wcblx84sJp33oE+fRrKT58Oq1bVUFOTWfvx2jPRu3x5DZMmwdlnpy5/4IHVnHwyTJxYw2OPwYgR6etfuBA6dEiuPxe9sdtt2sDXX2d+fbzYHjlypCf3Vz7bn34K++5bTdu2TY/36lXDVVfBN99U07EjXHrpSE44obh6s92ura3l0ksvDYyeTLaj+4KiJ5G+mpoa6uvryQlVzfuDPUDexIx+JuXnAFsnOaaFYNy4cSmP//CD6qJFiY+tWqXaurXq5s2N9195pWrz5qp33NH0nLo61R13bNjeuNHqWL3aO82JuPJK1b/9LbNyxxyjetppqvffn1nd77yjesghyY/nojeWl14yTX6Sr2YvuOIK1WuuSX58xAjVG29UPekkVRintbX+afOCIFzjbAmb5ojdzNhme+XqeQioU9V/JTooItvGfO+HzR/43qO2MyL6xEzG44/bwFoi6uvNZx8/yDZggMXln3NO03N23RWWLbO1c8Fi/rt0sTh/rzQnItMJXKNGwa23wkEHNXVJJSNdKGcuemMphqsnX81eMG4cHHpo8uPnnw9XXWX30/XXV3Pjjf5p84IgXONsCaPmbMjb1SMiBwGnAVNEZDKgwJ+AHbCn0H3ACSLyW2ADsA44Kd92vWbiRMuEuWqVGc9Y5syxSUvxHHus+WUTZaps1gz69IGpU+2Puq6u8P59MP977KIuiVi2zLJs7rKLGdpMfciFjOgBG8gstzj+lSstcGDAgORl+vWDhx6Ck06y9Ny9emUfKOBwxOJFVM+HqtpcVatUdR9V3VdV31TVeyNGH1W9U1X7RI4fqKqJBn8LSqxvLBGTJ5vBT1Qs2uOPRwQ6d05eZ9TwA0yZkv1szHSaE5HJ4G5tLfTt2/Bwmj8fvs/g/Sud4c9FbyzlGMdfU2OGPVWa62iyvjZt4LPParj4YrjpJr8U5k+xr3EuhFFzNriZu1gvasoUuOACGD266fH6+sQ9/nTsuaf15sAeAH5Mw88knHPyZJt5DJbY7YADLFVAOvzo8ZdbVM8zz8Bxx2V3zkUXwUsvle+cB0f+lI3hT+WzmzHDfNcnnJDY8M+Zk7jHn47YHv/UqbadDbn6+NP1+CdNajD8kHymcTzpDL8XPv5yiuNfuxZefRVOPDHzc6qrLbrnlFPg7rsLp81LwugvD6PmbCgbw5+KqCHs2xeWL7cefiz59PinTrV47JkzG9ItF5JkPf6amoaJQLE9frBxijFj0j8wFi9OvOSiV5RbHP+rr9rbVvzCPJlwySVw773ldb0c3lE2hj+Vz27yZNh3X/N5DxnStNefa49/m20ssdu771o2zrZtszvfSx///fdbdMjatfZ7YiejVVdDz572G/+VMC7LSLR6WL56YymGq6eYvtxRo6znng1RvbvtZvfsqFHe6/KaMPrLw6g5G8rG8Kci1vUxaJClYIiyfLnl1u/UKbe6+/SBp57K3s2TK8l6/DNnmsG/+WaLBmnZsuFY27bma/70U5uFnOieX7/eHihbb10w6WWVnXPpUhg71hbfyZX/+i947jnvNDnKiGyC/v34AHr22arLl+c9pyEjNm9W7dBBdelS254yRbV374bjkyer9umTe/2XXKJaUaF69dX56cyUFStU27ZtPNks+hvvuMMmnJ19dvLzX3tNdYcdml7/r79W7datIJJ/YuNGVZGmE+VKjfXrbSLclVfmV8+SJXZvbdzojS5HeKFIE7g85YknLPeNH+uxzJlj7pFttrHt3Xe3/DvffWfbufr3o+y5p701+NXjb9/eInWWLWvY9+235sY6/3z7LbH+/XiGDk2c/3/JksL698HyALVoYWMipcwFF9gCPjfckF89XbrY/8mUKd7ocpQPgTT8995rSasef9y7OpP57OrqGhvl5s1twO3TT207V/9+lGjduRj+XP2MPXrYwu1RZs2C3r3tt40ZA7/+derzzz3XlpCMZcmS9IOQXvhF/Xb3+O3LXbTI3DOPP24P42yJ1/uzn9l6zEEmjP7yQmp+5x2bMFpMAmn4Tz7ZekOJ1rX1mqhRjKV//4YIGC96/J072yxZv4g3/DNnNvzGykobRE1FVZUN5C5c2LAvE8PvBaUe2TNtmnUCvMpwesgh8P773tTlKDzr1sGZZ8LTTxdXRyANf+vWFlf/7bfe1ZksLjeR4R8wwAy/qkX89OqVe7sdOlgKhdgFWTIl11jiRD3+bB48zZtbpM/YsQ37MjH8XsQ++522we947WnT8gvrjdd7yCHW4w/gMtU/EcaY+EJpvuMOi6z7+uuCVJ8xgTT8YD53Lw1/MpL1+D/91CJdvvsOjjwyvzZiI2j8IJmrJxsOO8xeSaO4Hr83TJ/u7XyOnj3tYTlzpnd1OgrDihWWGPGWW5zhT0rnzg2ZLb0gmc8ukVHs0sXCFs89Fx54wGLxi4FXPv6ZM7N3NUUNf7Qn6aeP30/D77f/Od8efyK90V5/UHE+fuPRR+GIIyx4otiG37OFWLxmq60sumPduvQ+6VxZv9582Tvs0PTYoEEWITNwYGHaLiSxhl8VZs/Ovse/666Wwyh6rl89/lLP0Jmv4U/EPvs0pAZxBJdPP7VlNbfbzuZxxK/J7SeB7fFHM19GwyrzJZHP7quv7FW5RYLH3z33wD//6U3bueKFj3/RIhtIbN8+uzpEYPBgyxUP/vn4/e7x++l/XrHCQnt79Mi9jkR6e/WyezmoOB+/Ec0Q0KKFheGmS59eSAJr+MF7d088qXzfLVs2XXglLGy/vd1Umzc3jujJlv79GxaU97PHX6o+/mgO/VzCOFOx447BNvwOWLPGIgT3iKw23rNncd09ed+CIrK9iIwVkS9FZIqIXJKk3G0iMiuy4HpVJnV37uzdAG8in10ug55+kqufsW1b6+V/843l3t8jx6Xt993X4o03bLDearq0FS6OPzVeuHkS6d1xR5tvEtTIHufjhy++sP/7qGsn9IYf2Aj8t6ruCQwELhSRRmtNRRZY30lVewPnAfdkUnGhI3uCbvjzIeruef55GDYstzqqqsxYLVhgRr95c281JqKUo3oK4d8HGw9r187GqxzBZNJXnqICAAAgAElEQVQk60hFCb3hV9XFqlob+b4amAZ0jys2HHgsUmY80CF2Hd5keNnjT+Szy2XQ00/y8TP26GGDSVOn2oBSLrRta/7jd97JzM3jVRx/oQ3/iy9axlTw1//sRShnMr1B9vM7H38JGv5YRKQSqALi13PqDsQEGLKApg+HJvjh499558LVX0x69IDbboNjjskvHHW//eCNN/zx74M/rp7nnoPLLvPfNTJ9euHWXe7Vy9w9jmASHdiNUjKGX0TaAc8Cv4v0/PPGS1dPvM/uhx9swLJnT2/qLwT5+Bl79LDBxBNOyE/DfvvZ+gSZGP6wxPEvXWpvQm+95Z//efNmG9zLZxY4JNcb5B5/ufv416+3h37s0qvxc238xpM4fhFpgRn9f6vqSwmKLABig9i2j+xLyIgRI6isrGTqVJg9u4KamqqfXr2i/yHZbkeJbnfrVk2PHvDBB7nVF/TtHj2q2WoraN26hpqa3OsTqWHVKth2W3/0L11aw4oVAIVr7z//gUsuqebGG+H442sL+nui27vuWk2HDjB+fH711dYm1rvjjtW8/3768994o4bvv4fTTivs743drq2tLfrfQ772Ip/6Zs6EnXaqpk2bhuN7713N11/np6+mpob6+OUCMyWbHM7JPpj//h8pjg8FXot8HwB8kqLsTzmm335btbras5TVjXjrLdXDDitM3UGgvl71zjvzr2fVKsuRf8st+deVCdddp3rVVYVto1s3uz69eql+/HFh24ry0Ueq/foVrv6xY1V/9rP05f75T1sLwOEf99+veuaZjfdt3qzarp13647gdz5+ETkIOA0YLCKTRWSSiBwpIueJyLkRS/46MEdEZgP3AhdkUncho3rmzk08Y7dU2GEHy/ueL+3amV/aTx9/IV09mzfbuFG3bnDssf5ltiz0/Zapq+fNN21uxsaNhdPiaEzsCn9RROz/rK6uOJq8iOr5UFWbq2qVqu6jqvuq6puqeq+q3hdT7iJV3VlV91bVSZnUXcg4/jAY/njNxeLKK+Hgg9OX80JvoVM2LFtmD7OWLaFvXxgzpqZwjcXg1f2W7Bpvv7090FI9NNetgw8/tEVgpk3LX0umBOU+zgYvNccP7EYZNgyefdazZrIi0DN3O3Uyw1+I6Iv6+vwWWCknzjgj/0HJTCl0j3/p0oa3l759/RsQLfT91ry5BSqkcvm+/7795sGDYXx83F0R2LAB7rsvuBPPvGDjRpu8VZVgyuqpp9p63Js2+a8r0Ia/VSvrAdpgX35EB0eihKHHH6856Hiht9Bx/EuXWvZVsBnNCxdW+7LUo1f3W6pr3KtX6mUY33zTUoz369ewwpwfJNM8ejScd56tuBc0vPrbmzEDundPnCtr993tXixGZtVAG34onJ8/DIa/HCl0HH9szqG2bS2szo9c9n7cbxddBH/4gyWCS8Rbb1lKYL8NfzKefBIuvBD+/OfUbyphJpF/P5ZTT/VnpcF4Am/4vfLzx/rsNmyw6e3d004hKy5h8416odcPV0+0xw/QtWtNwRcrVzXDVkgfP9hg9dFHw/nnN3WfzJtnD7399oO997aH3Zo1Tet4803vr38izWvXwmuvwTXXwO9/b+s/DBkCt9/ubdu54tXfXvyM3XhOPtnSqvz4oyfNZUwoDL/Xs3fnz7e0qMXKhe1Ijp+uHjD3yBdfFK49sNTirVplnxo7F/73f83YfPBB4/2jR1vqjubNTUufPlYulo0b4aSTGtabLiSvvGLZX7t0McP/wANwyinBMfz5oAqvv24RZOkMf48eFmH25Zf+6YMQGH6vXD2xPru5c8MxsFuOPn4/XT0Aw4ZV/9Tj92rth3i8HNhNd43btIETT7SeeyxR/36U/v2bGvhPP4WVK70f8E6kedQoM/RgD6NDD4WzzrL/g4ULvW0/F/K5l8eOtVQpxx1n2XFTuXrABn4j8/J8I/CG38uQzijOvx9c/Hb19O1rPf6HH7Z7ohCv3H7fb0OGWA8/ysaNlmhvyJCGfSedBP/3f43HN0aPtreBQuf8UTU9xx7beH+zZvCznzUk0Asr0UWcuna1jmvnzqnLZ2v4N2+2/7+rrrLorLvuyl5jKAz/0qX51xPrs/PK31poytHH77erZ+7cGr77Di6/3NZZnjzZ+za9NPyZXOMBA8ygR99gJkywOP/ttmsoc/DB8Le/wdChDa7UMWPgV7/y3vDHa16wALbcMvH6DtXVwTD8ud7LixbB22/D2WfbA+Dzz9Ofk63hf/ppyyy8di2MGJHbQH3gDf/++zcs/+cVrscfXPx29TRrBsOHw/332+v5Rx9536bfc0ZatrQF2N95x7bffNOieeI55xw4/nj7d8UKe/M588zC9/ijK5ElYtAgCFl/pxEPPmiutvbtbXbullumP2fvve0Bkcl8hg0bbED8llvsrWLaNHjkkRyEZpPfwY8PMbl6VFU3brTcKtOnJ89TMWWK6nHHqW7alD6nharq4MGqo0dnVtbhLwsXqm67beHq32qr5PlRHn9c9Ze/9L7NYcNUn3/e+3pTcdttquecY9/797e8V4lYv161b1/VE05QPeII1XnzVLt2Lay2O+9UPffcxMc2blStqFBdtKiwGgrB5s2qO+ygOnFi9ud276761VfJj19yiWpVlf2fDh7c9Dh+5+opNM2bmz/riSca74/Odlu40ELYxo2zV9pEzJ/fOHTN9fiDSyFTNqxbZz78ZNE1Bx5oKQ28nkk6e7Z/M5+jHHEEvPqqhQv+5z/JU260bAmPPmqL0wwZYu6gZcsK+9aVqsffvLlpLcakpnyZONGuZ7rB3ESkcveo2v/PueeaW+7WW/PTCSFw9QCcdppNcoj+QS5fboa7e3eLTjjvPEtI9sILTc9VtUGks86qAexB8e23wc7DH6UcffyFHNyN+vdFGvbFaq6stGNz53rX5oYNFiWTzNBlS6bXeNddzegPHmyhgqkW46mqMsNy1lnm+kqX+iFb4jVPn576ehx/PPzmN9ah8yO0NBG53MsvvGDaY++vTEll+KMD8OefDy+9lDo8NFNCYfj32896AlH/6w03WGjahx/aA+HKKy106vnnm/bW3n3XejBvvWV+zHvusQdJ69b+/w5Helq1sl755s3e1x0/sBuPSEOv3ytmz7ZYbb/vNxEYOdJ6ial+c5Sjj7YIFCj8al6pevxgA6OzZ1sSsxNPtL/fMPD882b4cyGV4X/rLXsby+WBkpRs/EJ+fIjz8Uf597/N9/jgg6qdOqkuXtz4+ObNqj16qE6d2nj/sGGqd9+tesopqjfeaP7jurokjjRHIGjdWnXNGu/rfeUV1aFDU5f5xz9UL7jAuzaffVZ1+HDv6vOD889Xvf325Mc3bsy97rVr7f93w4bMyl90keppp+Xenl/U1ZmfPtNxxnhmzzb7lui6HH206jPPpD6fUvPxRzn9dPj3vy0XyR/+0DQ/vIj1+mPdPbNn21vCmWfaGqvXXGPLn+W74LWjsBTK3bN0aUOvNhkHH2zheF69cdTVWTK4MLHjjql7/EcfbVk1c2HWLHujaJHh2n8332zuHr/WTciVF14w+9MsR4u60072FjRqVOP969fbeMdhh+WvMZbQGH6wKedz5ljMdSJ+9St7OEQHfv/5T/MVtm0La9bUcMwxyc8NIuXo44fCxfKvXAkdOjTeF695//0tX/8rr3jTpteG3497IpXhHz/eDNE992ReX6zmdP79eNq2NYPqt+HP9jq/8gr84hf5tXnNNebGjk3T/PHH1lHdeuv86o7HE8MvIg+KyBIRSZj1REQGicjyyOpck0Tk6lzbisbHJuKgg2xSyDPP2CDuqFFw6aUNx194waIdHMGmULH8a9akj6sWgT/9CW680ZvonjD2+FP5+G+6Cf7+d5sclkuagXT+/UTsv7+tGhZU1q61ORAHHphfPYceauMxTz9t29OnwyWXWIfWc7LxCyX7AAcDVcAXSY4PAl7OsK4sPGNNefNN1d13t7jXSy/NqypHkdh996ZjNV5w5ZWqN9yQvtymTaZhzJj82tuwQbVNm8KMVxSSZctsPdh4f/XUqapdutjv+fOf7W8sGRs3qn77bdP9p52m+sgj2emZPdvG7/zi4otTzxuK5913vVtPeexY1S23VN1rL9XOnVXvu8/GL9NBMXz8qvoBkG7s3csx6aQMGWKv8/fdZ2MBjvBRKFfP2rWZzaRs1szeFB96KL/25syxfC1t2+ZXj99UVNjf0NdfN95///0WUti2raUKePJJ80EnYtQoyzp50UWNU67MmAG77JKdnl69YNUqm3XtBy+9lF3+mw8/NG+DFxx6qHkrHn3U5iX95jceR/NE8NPHP1BEakXkNREp2MuviPn2b7mlcW6SsPnLIXyavdLrp6snmebdd88/nr8Qbh6/7ok+fWDq1IZtVfNjR8MVe/WC3XazTJSJeP11+OtfLYT69NNrfqpj1qzsDb+IuXsmTsz+d2SLqq3V8cgjNY06H6tXW+ro119vmjvMS8MP5s7eZ5/Cpvnwy/BPBHqqahVwB/BiIRsbMAAuvriQLTgKSaGiejLx8Ufp3t2SieVDGP37Ufr0abyM4/TpNr+ib9+GfYMGJc5ttGmTJXw7/XTzUUfTPH/3nRnxXAYqDzjAHz//8uV2//Xu3ThC8LnnrDM5cqT9n956a8N8k48+yt+/7zcZBlXlh6qujvn+hojcJSJbq+r3icqPGDGCysjjrqKigqqqqp/yY0d7POWwXV1dHSg9fulduxbWrPFe35o18NVXNdTUNM63XlNT06T8gAHVLFoEY8fW0KxZbu3V1UH37o3by/f3JNPr9XafPtWMGdOwPWFCNcccA+++21B+4EC45poaDjus8fnTp0OXLtX06AFTp9Ywb549DGbNgm23reHdd7PXs//+1TzySOHv55dfrqF9e7j88mruvRe6dbPjo0dX84c/QO/eNcyfD48+Ws1HH8Hw4TW0agXduhVGT7Lt6Pf6XKdYZzMgkOoDVAJTkhzbNuZ7P6A+RT3Zj4g4SorTTlN97DHv6z3ssOyS83XqpLpkSe7t7buv6vjxuZ9fTCZMsORtUQ45RPW11xqX+fZbS3oXP6Hr+utVL7usYbuyUnXGDNVHH1U99dTc9MydaxOcMhnozIexY+23/vCD6jbb2MDypk02qD1nTkO5H36wAd0DDlA9/fTCasoEijG4KyJPAh8Bu4jI1yJytoicJyLnRoqcICJTRWQyMBI4yYt2syH2SRkWwqbZK70dOiRfMDwfsvHxQ37uns2bzT3i9WRBv+6JPfawHDEbNsD339s6BYce2rhMp042gBs7FgBN00B37VrDl1/ahMqdd85NT48e9tZQ6NW5Fi+23/TxxzWccYYN8H/xhd2TsT73Vq3g2WfNjeWlf98vPHH1qOqpaY7fCdzpRVuO0qeiwgYFvSbTqJ4oUcOfS7bFuXPNMG61VfbnBoG2bW3xltmzbRJRdbVFW8Vz4IF2fO+9bXvFCsstf8ghDWUqKy1R3KxZNus3F0TsYTRjhv2/FIpFiywSC2ydgsMPt3sm0fyfHj1sTd34LAJhIFQzd/Mh1kcaFsKm2Su9HToUxvAn6vGn0pxPj79QA7t+3hN9+tgkrZtuajwRMpaBA83wR/n0U8seGfuQOOqo6rx7/GAPEC+zhiZi8WIz/NXV1eyxh81ivvXWxstWxtKzZ+rMp0GlbAy/Izz46epJRRANv5/06QN/+Yv1bAcPTlwm3vBPmGAROLHsuWdDj79379z1pMsh5AVRwx/l17+2OQTxbq6wUzaGP2z+cgifZq/0FsrVs2ZN08lUhfLxF8rw+3lP9Oljfv7rr09eZo89bGJVdN3eCRMs5j6Wb76pYdq03EM5o/hp+KPX+eSTbTJassV7wkrZGH5HeCiEq0fV9fizZdAg6/GnGrxs3tz8/2PG2HaiHn/r1rZwUu/e+c1C9cvwd+vWsN2mTYFy5RQZUS8yUXmIiGjQNDn85eOPzac8frx3df7wgz1QkqUYSMTnn9skpNiJTJmgam3NnQsdO2Z3bhi5/35b+vT//s/eEr79tqmBHz7csp7GL6GaDfPm2Yp7hYzs6dLF/r/DNmArIqhqxo9V1+N3BI5CuHqyjeiBxj3+8ePN8GTC/Plm5MrB6AMMHWohnJ98Ym6eRL36/v1tlal82G47Cy0t1NKcGzbYal+dOxem/iBRNoY/bP5yCJ9mL+P4vTb8ydw8qTR36mQPjHXrbAnDa6/NrK1CunmCeE90724RN7fd1tTNA6b5T3/KP2li8+Y20OzlmsixLF1qRr9582BeZy8pG8PvCA+FiOrJ1r8P1nPdbjt45x2L737hhcx0TZrUOKdNOXDMMVBT03Rg12sK6eeP9++XMmVj+MMWEw/h0+yV3rZtYeNGS4LlFYkieiC95u7dLdvrKadYLHcmPupCJu0K6j0RnZiVqMfvpebKysIa/mgoZ1Cvs1eUjeF3hAcR7909ufT4wQz/2LFw0knm7rn33tQrc6na4HTYsjXmywEHWA77Qs6qBevxF2oSV3wMfylTNoY/jD67sGn2Uq/X7p5cfPxghqxHD0v1feih5vP/5JPk5WfNsoHd2LUgvCSo90SzZvDb3yY+5qXmQrt6ooY/qNfZK3xJy+xwZIvXkT25RPWA9WS32cYMG8CFF8K//mUzVhMRxtzsYaLQhj+fmcVhwsXxOwLJ4MFw1VVw2GHe1PfYY/D22/ZvPqxcaX7m2lrL0xLPuefawO5FF+XXjiMxS5ZYCohvv/W+7uOOg1NPDeeELRfH7ygJ/HL1ZEv79rbe7B13JD7uevyFpUsXWwZx7Vpv6/3xR3j33XCmWM6FsjH8YfTZhU2zl3q9dvUki+rJRfMll1ie9tWrG+9fvtxizAsZyhm2ewK81SxifnivF14fN87WEI6OzYTxOmdD2Rh+R7gISlRPIioroV8/W3w8lg8+sP0t3MhZQena1fzxXvLCC+bqKRe8WoHrQRFZIiJfpChzm4jMEpFaEclz8nb2hDEuN2yavdTrl6snV82//CU8/3zjfa++aukLCknY7gnwXrPXhn/TJnjxxcaGP4zXORu86vE/DPw82UEROQrYSVV7A+cB93jUrqNE8brHn2tUTzKGD4fRoy2dA9hSiy+/DMOGedeGIzFeG/5PPrGxg3wWiQkbnhh+Vf0AWJaiyHDgsUjZ8UAHEfE1/10YfXZh0xx0H38ucfzJ6NwZ9tvPjD/AxImmudDhgGG7J8B7zV4b/pdftgd5LGG8ztngl4+/OxCb23BBZJ/DkZCgRvXEcvzx8Nxz9t319v3Da8P/7rvehQ2HhbIZ3A2jzy5smr328fsR1ZOP5uOOM4N/3302OOiH4Q/bPQHB9vGvWWP59/v3b7w/jNc5G/yKP1gA9IjZ3j6yLyEjRoygsrISgIqKCqqqqn76j4i+grnt0t6uqKhmxQrv6luzppott/RWb/fucO21NTz2GKxfX03//sG5fqW8vXAhLF7sTX333FPDjjtCmzbB+X2ZbEe/1+eauEhVPfkAlcCUJMeGAq9Fvg8APklRjxaCcePGFaTeQhI2zV7qnTlTtVcvz6rT/fdXHT++6f5yvsZ+4bXmOXNUe/b0pq5rr1X94x+b7g/bdY7YzYzttSc9fhF5EqgGOonI18C1QMuImPtU9XURGSois4E1wNletOsoXYIe1eMoHttua64e1fzW8AV47z34/e+90RUmXK4eRyBZv96yXP74Y/5/3GCTrmpq7F9H+KmosGRtmS5vqQqPPAKjRtl8i5Yt7d7aemtbXrNDh4LKLTjZ5upxcwwdgaRVK5sBu25d4kHZbClEVI+jeEQHeNMZ/scfh6++sgHcujoz9HfeCZddZiG4u+wSfqOfC2UT1RM7KBIWwqbZa70dOtji117gZa6eYhI2vVAYzZlE9kyYAJdfbouo9+tn2/ffDzfeCF9+Cf/zP8kjscJ4nbPB9fgdgaVPH+uV5buq0+bN8MMP0KaNN7ocxSfe8M+LzBLqERM7eOWV8Je/WKrsKLvtBqedBlVVdvyaa3yRGzicj98RWG691bJdJkuBnCmrV9uA4Jo13uhyFJ9LL4UddjCXzfz5lgq7c2f49FNzEb79NlxwgfXst9ii8bmrV1uHYtCg4mgvBC4fv6NkGDKkISVCPriIntIj2uNfuRKOPNIWvunY0ToJ8+bZSml/+1tTow8WNFBKRj8Xysbwh9FnFzbNXuvt29f+sOfMsV7agw/mVk+qgd1yv8Z+UEgf/w03wL77wh/+YIu933CD9f7POy+/lbTCeJ2zwfn4HYFFpKHX/+WX9od95pmJe3GpcBE9pUfXrjZYu2SJReyIwK67ws03W1DACScUW2GwcT5+R6B5/HF7ZV+xwmKx33sv+wyY48fDxReb/9dRGtTWwj77wF//Wr4DtLE4H7+jpDjiCJgxw2Kv99oLZs/Ovg7X4y89Kivh8MMtJNORPWVj+MPoswub5kLo3XZbM/zHHWcLZXht+N01LjyF0FxRAWPGFO6BHsbrnA1lY/gd4SXq2undG2bNyv78lSvLc3amw5EM5+N3hIZXXoG774bXX8/uvLvusgHAu+8ujC6Ho9g4H7+jZNl559x6/CtWuB6/wxFL2Rj+MPrswqa50Hp79YKvv7bcK9mQyvC7a1x4nObgUTaG3xF+WrWCbt3M+GeD6/E7HI1xPn5HqDj8cFs448gjMz/n1FPh6KMtOZfDUYoUxccvIkeKyHQRmSkiVyQ4PkhElovIpMjnai/adZQfvXtbSOdXX8HSpZmd43r8Dkdj8jb8ItIMuAP4ObAncIqI7Jag6Huqum/kc0O+7WZLGH12YdPsh96dd7bZmrvuanlZMsH5+IuL0xw8vMjV0w+YpapzAUTkKWA4MD2unAcL6DnKnZNPhp49bem822/P7BzX43c4GpO3j19Efgn8XFXPjWyfDvRT1UtiygwCngPmAwuAP6hqXZL6nI/fkZa5cy0L44IF6cv27Anvv2/52x2OUiSoa+5OBHqq6loROQp4EdjFp7YdJUiPHtaTz6Q3v3IltG/vjy6HIwx4YfgXAD1jtreP7PsJVV0d8/0NEblLRLZW1e8TVThixAgqKysBqKiooKqqiurqaqDB95btdnRfrucXYztee7H1BE3vbrvBE0/UsMceycuPHVsTMfyJj48cOdKT+8uv7bDprampoba2lksvvTQwejLZju4Lip5E+mpqaqivrycnVDWvD9AcmA3sALQEaoHd48psG/O9H1Cfoj4tBOPGjStIvYUkbJr91nvGGaoPPZS6zIoVqu3aJT/urnHhcZoLT8RuZmy3PYnjF5EjgX9hUUIPqurfReS8iJj7RORC4LfABmAdcJmqjk9Sl3qhyVH63HQTfP+9rc2bjHnzYOBAW5fV4ShViuLjV9U3gV3j9t0b8/1O4E4v2nI4ouy+OzzwQOoyLqLH4WhK2aRsiPWNhYWwafZb7x57wLRpqcukM/zuGhcepzl4lI3hd5QevXrBwoWwbl3yMq7H73A0xeXqcYSaPn1sXd6qqsTHR42Cl16Cp57yV5fD4ScuH7+jrOjTBz7/PPlx1+N3OJpSNoY/jD67sGkuht4jjki9IteKFaknb7lrXHic5uBRNobfUZoccwyMHg0//pj4uOvxOxxNcT5+R+gZMMAydR5+eNNjF11kmTwvvth/XQ6HXzgfv6PsOPZYePnlxMdcj9/haErZGP4w+uzCprlYeocNg1degUQvii6Ov/g4zcGjbAy/o3Tp08eMfqLJXK7H73A0xfn4HSXB8OFw9tnwi1803l9VBQ8/DPvsUxxdDocfOB+/oyzp1g0WLWq63/X4HY6mlI3hD6PPLmyai6m3WzdL3xCP8/EXH6c5eJSN4XeUNol6/Kpu9S2HIxHOx+8oCV55Be65B157rWHf6tXQpQusXVs8XQ6HHzgfv6MsSdTjd/59hyMxnhh+ETlSRKaLyEwRuSJJmdtEZJaI1IpIklyKhSOMPruwaS6m3u22a2r4R42yWb2pcNe48DjNwSPvFbhEpBlwB3AYsBCYICIvqer0mDJHATupam8R6Q/cA6T5k3Q4MqdLF/juO9i4EVq0sO833wzvv19sZQ5H8Mjbxy8iA4BrVfWoyPYfsbV2b44pcw8wTlWfjmxPA6pVdUmC+pyP35ETXbvCpEnW+//d7+whcKdb8NNRBhRjzd3uwLyY7flAvzRlFkT2NTH8DkeuRP387drZpK3//KfYihyOYFI2g7th9NmFTXOx9UYN/2efQd++sM026c8ptuZsCZtecJqDiBc9/gVAz5jt7SP74sv0SFPmJ0aMGEFlZSUAFRUVVFVVUV1dDTT8h2S7HSXX89128Le7dbPtlSuhX7/Mzq+trQ2M/lLUW1NTQ21tbaD0ZLIdJSh6Eumrqamhvr6eXPDCx98cmIEN7i4CPgVOUdVpMWWGAheq6tGRMYGRqppwcNf5+B25cvXV0LIl1NbCr34Fp5xSbEUOhz/4HsevqpuAi4DRwJfAU6o6TUTOE5FzI2VeB+aIyGzgXuCCfNt1OOKJhnROmAD94keZHA7HT3ji41fVN1V1V1Xtrap/j+y7V1XviylzkarurKp7q+okL9rNhvhXuDAQNs3F1tutG0ycaDN1e/XK7Jxia86WsOkFpzmIlM3grqP06datobcvGb/0Ohzlh8vV4ygZ5s6Fykr485/huuuKrcbh8A+Xq8dRtnTtav86/77DkZqyMfxh9NmFTXOx9bZqBfvvD/37Z35OsTVnS9j0gtMcRLyI43c4AsOECcVW4HAEH+fjdzgcjpDjfPwOh8PhSEnZGP4w+uzCpjlseiF8msOmF5zmIFI2ht/hcDgchvPxOxwOR8hxPn6Hw+FwpKRsDH8YfXZh0xw2vRA+zWHTC05zECkbw+9wOBwOw/n4HQ6HI+Q4H7/D4XA4UpKX4ReRjiIyWkRmiMhbItIhSbl6EflcRE+lxDgAAAaxSURBVCaLyKf5tJkrYfTZhU1z2PRC+DSHTS84zUEk3x7/H4G3VXVXYCxwZZJym4FqVd1HVYuSOzG6VmmYCJvmsOmF8GkOm15wmoNIvoZ/OPBo5PujwC+SlBMP2sqL5cuXF7P5nAib5rDphfBpDptecJqDSL7GuIuqLgFQ1cVAlyTlFBgjIhNE5Dd5tulwOByOPEiblllExgDbxu7CDPnVCYonC8c5SFUXicg22ANgmqp+kLXaPKivr/ezOU8Im+aw6YXwaQ6bXnCag0he4ZwiMg3z3S8Rka7AOFXdPc051wKrVPUfSY67WE6Hw+HIkmzCOfNdiOVlYARwM3AW8FJ8ARFpCzRT1dUisiUwBPhrsgqzEe9wOByO7Mm3x7818AzQA5gLnKiqy0WkG3C/qh4jIjsCL2BuoBbAE6r69/ylOxwOhyMXAjdz1+FwOByFxc3cdTgcjjLDGX6Hw+EoM5zhdzgcjjKjbAy/iIwttoZkiMhxkYFyRGQbEXlMRKaIyNMisn2x9SVCRLYWkWtE5NdiXCUir4rIrSLSsdj6kiEiPxeRu0Xk5cjnbhE5sti6khHRe46IVMbt/6/iKMoe97fnLV787ZXk4K6IfBG/C9gFmAGgqn19F5UCEalT1T0i358GPgH+H3A4cJqqHlFMfYkQkdeBKUB7YPfI92eAI4C9VXV4EeUlRERGYvfBY8D8yO7tgTOBWar6u2JpS4SI3AgcDEwCjgVGqurtkWOTVHXfYupLhPvbKzxe/O2VquF/GVgJ3ACsw26+97E/IlR1bvHUNUVEZkQS3SEiE1V1v5hjtapaVTx1iYnqEhEB5qtq9/hjRZSXEBGZqaq7JNgvwExV7V0EWUkRkSnAPqq6UUQqgCeBGap6mYhMVtV9iiyxCe5vr/B48bdXkq4eVR0GPAfchz0B64ENqjo3aDdehBoRuU5E2kS+HwcgIocCK4orLSnNIq+VPYB2UVeEiHQCWhZRVyp+EJEDEuw/APjBbzEZ0EJVNwKo6nKs199eRP4fAb3G7m/PF/L/21PVkv0AWwL/wGYUzy+2nhQ6twD+Anwd+WwGVmE9vJ7F1pdE8ynAksjnl8DbwBhgAXBusfUl0bwvMB6oA0ZHPtOw1/v9iq0vgd5XgUEJ9t8AbC62vjTa3d9e4TTn/bdXkq6eeERkb2Cgqt5TbC3piCxm00JVvyu2lnSISHPMXbhRRFoAVcACVV1UZGkpieSVir4eL1DLLBs4Ir1QVHVdgmPdVXWB/6qyw/3tFYZ8//ZK1vBH/F/9iPkDBz7VgP7gsOmFcGpOhojspqrTi60jU8KmF8KnOWx6IXPNJWn4RWQIcBcwCzNGYNEbOwMXqOroYmlLRNj0Qjg1p0JEvlbVnsXWkSlh0wvh0xw2vZC55nyzcwaVfwGHqw0s/UQkYdzrWAhUkAibXgihZhG5LdkhoMJPLZkQNr0QPs1h0wveaC5Vw9+ChjjtWBZggzlBI2x6IZyazwb+B1if4NgpPmvJhLDphfBpDpte8EBzqRr+h4AJIvIUMC+yrwdwMvBg0VQlJ2x6IZyaJwBTVfWj+AMi8hf/5aQlbHohfJrDphc80FySPn4AEdkDGEbjgceXVbWueKqSEza9ED7Nkan5P6jq2mJryYSw6YXwaQ6bXvBGc8kafofD4XAkpiRn7opIBxH5u4hMF5HvReQ7EZkW2Re4AZuw6QWn2Q/CphfCpzlsesEbzSVp+LGERcuwheC3VtVOwKGRfc8UVVliwqYXnGY/CJteCJ/msOkFDzSXpKtHYhIvZXOsWIRNLzjNfhA2vRA+zWHTC95oLtUe/1wRuVxEto3uEJFtReQKGiJQgkTY9ILT7Adh0wvh0xw2veCB5lI1/CcBnYB3RWSZiHwP1ABbAycWU1gSwqYXnGY/CJteCJ/msOkFDzSXpKsHLGcFlkLgE1VdHbP/SFV9s3jKEhM2veA0+0HY9EL4NIdNL3ig2atUoUH6AJdgK/68CNQDw2OOTSq2vrDrdZqd3lLRHDa9Xmku+o8o0IWZArSLfK8EPgN+F9meXGx9YdfrNDu9paI5bHq90lyqKRuaaeT1R1XrRaQaeFZEdsASGQWNsOkFp9kPwqYXwqc5bHrBA82lOri7RER+WncycpGOAToDexVNVXLCphecZj8Im14In+aw6QUPNJfk4K6IbA9s1AQrK4nIQar6YRFkJSVsesFp9oOw6YXwaQ6bXvBGc0kafofD4XAkp1RdPQ6Hw+FIgjP8DofDUWY4w+9wOBxlhjP8DofDUWY4w+9wOBxlxv8HnPFKXIF7mqUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8317a8c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot unified inflation, given start:\n",
    "plot( inf_av[start:] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the dramatic drop in the unified inflation rate \n",
    "during the Great Recession."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break-even inflation \n",
    "\n",
    "BEI is computed using only data from the bond market. \n",
    "The key BEI uses 10-year US government bonds, \n",
    "taking the difference in rates between the \n",
    "usual on-the-run bond and the TIPS issue. \n",
    "TIPS is an abbreviation for \"Treasury Inflation Protected Security.\"\n",
    "\n",
    "TIPS allow us to observe *real* interest rates\n",
    "being traded in the market. \n",
    "(Notebook https://git.io/gold makes a conjecture \n",
    "that real gold prices is a stationary time-series \n",
    "bound by real interest rates.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "bei = todf( ts[m4bond10] - ts[m4tips10] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa83303ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot Break-even inflation rate\n",
    "plot( bei[start:] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "               Y\n",
       "T               \n",
       "2017-08-01  1.78\n",
       "2017-09-01  1.83\n",
       "2017-10-01  1.86\n",
       "2017-11-01  1.85\n",
       "2017-12-01  1.90\n",
       "2018-01-01  2.04\n",
       "2018-02-01  2.10"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail(bei)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Studies comparing forward-looking BEI and realized inflation generally show \n",
    "*BEI overestimates realized inflation*. \n",
    "\n",
    "What is the correlation between BEI and average inflation? \n",
    "Not much. BEI seems to lead unified inflation, cf. circa 2009. \n",
    "Also, BEI appears more stable relative to unified inflation.\n",
    "\n",
    "It is worth emphasizing that \n",
    "**unified inflation is a \"rear view\"** while ***BEI is \"forward looking.\"*** \n",
    "Real money bets are made on the spread of the latter. \n",
    "\n",
    "#### Present: unified inflation and BEI\n",
    "\n",
    "Let us mix the rear and forward views equally at arrive at a **\"present view\"**, \n",
    "`bei_inf_av`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bei_inf_av = todf((bei + inf_av) / 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7b785cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot present view:\n",
    "plot( bei_inf_av[start:] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   Y\n",
       "T                   \n",
       "2017-07-01  1.668481\n",
       "2017-08-01  1.687450\n",
       "2017-09-01  1.782154\n",
       "2017-10-01  1.788856\n",
       "2017-11-01  1.820302\n",
       "2017-12-01  1.838068\n",
       "2018-01-01  1.913632"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Probably the best accessment of current inflation:\n",
    "tail(bei_inf_av)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our present inflation measure consisting of 1 part each of \n",
    "CPI, CPI core, PCE, PCE core -- plus 4 parts of BEI. \n",
    "It does have strong correlation with the Fed's favored inflation measure, \n",
    "see Appendix 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unified inflation *level*\n",
    "\n",
    "To readily access our findings above in other studies, \n",
    "we developed the synthetic pre-fabricated **m4infl** series. \n",
    "Each measure of inflation *levels* is first rescaled such that \n",
    "the most recent point is equal to 1. \n",
    "This eliminates the base year problem and the \n",
    "arbitrarily set level of 100 somewhere in time. \n",
    "Then we can take the average among inflation levels which \n",
    "will be by mathematical construction, *equally weighted*. \n",
    "The levels originate from fredcodes: \n",
    "`['CPIAUCSL', 'CPILFESL', 'PCEPI', 'PCEPILFE']`.\n",
    "\n",
    "As a very convenient by-product, the recipricol of m4infl \n",
    "yields *multiplicative factors useful for deflating prices*, see **m4defl**.\n",
    "\n",
    "(API note: The average between backward and forward-looking inflation *rates* \n",
    "is codified as **m4inflbei**.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "infl = get(m4infl)\n",
    "\n",
    "#  m4infl represents unified inflation level, synthesized\n",
    "#  from four different US government time-series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   Y\n",
       "T                   \n",
       "2017-07-01  0.985627\n",
       "2017-08-01  0.987997\n",
       "2017-09-01  0.990815\n",
       "2017-10-01  0.992491\n",
       "2017-11-01  0.994326\n",
       "2017-12-01  0.996198\n",
       "2018-01-01  1.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Latest levels of unified inflation:\n",
    "tail(infl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  From the level, we compute annual rates:\n",
    "inflrate = pcent(infl, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  FIRST variable:\n",
      "count    697.000000\n",
      "mean       3.527297\n",
      "std        2.466253\n",
      "min       -0.160953\n",
      "25%        1.720500\n",
      "50%        2.687746\n",
      "75%        4.481560\n",
      "max       12.016273\n",
      "Name: Y, dtype: float64\n",
      "\n",
      " ::  SECOND variable:\n",
      "count    697.000000\n",
      "mean       3.509197\n",
      "std        2.443296\n",
      "min       -0.183227\n",
      "25%        1.720457\n",
      "50%        2.669016\n",
      "75%        4.461897\n",
      "max       11.867394\n",
      "Name: Y, dtype: float64\n",
      "\n",
      " ::  CORRELATION\n",
      "0.9999565267346865\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      Y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 7.993e+06\n",
      "Date:                Mon, 12 Mar 2018   Prob (F-statistic):               0.00\n",
      "Time:                        09:19:31   Log-Likelihood:                 1640.9\n",
      "No. Observations:                 697   AIC:                            -3278.\n",
      "Df Residuals:                     695   BIC:                            -3269.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0147      0.002     -9.642      0.000      -0.018      -0.012\n",
      "X              1.0094      0.000   2827.171      0.000       1.009       1.010\n",
      "==============================================================================\n",
      "Omnibus:                       59.704   Durbin-Watson:                   0.145\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              316.153\n",
      "Skew:                           0.059   Prob(JB):                     2.23e-69\n",
      "Kurtosis:                       6.297   Cond. No.                         7.77\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#  VERIFY if our two methods agree, by linear regression:\n",
    "stat2( inf_av['Y'], inflrate['Y'] )\n",
    "#  (Think of this as an unit test, visible in a notebook.)\n",
    "#  'Y' is the default column label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result shows perfect correlation since 1960 (full dataset), \n",
    "thus our synthetic series **m4infl** works as intended."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometric mean rate of unified inflation\n",
    "\n",
    "The level form of unified inflation allows us to \n",
    "directly use the tools developed for financial assets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.8681557525558556,\n",
       " 1.8694514743803674,\n",
       " 0.5137664910401363,\n",
       " 10.311330360312478,\n",
       " 12,\n",
       " 180]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gemrat(infl[start:], yearly=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The geometric mean rate of unified inflation is 1.87% \n",
    "since `start`, the volatility is 0.51%, \n",
    "thus a 50 basis point move in a year would not be surprising. \n",
    "The most interesting statistic reveals that *inflation is leptokurtotic*.\n",
    "\n",
    "The arithmetic mean rate sought by the Federal Reserve \n",
    "is currently 2%, so the economy in this aspect is \n",
    "within policy expectations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Holt-Winters forecast for inflation\n",
    "\n",
    "For predicting inflation, it is preferable to use levels, rather than rates, \n",
    "as primary form of time-series due to linearity considerations \n",
    "(see plot in the next cell).\n",
    "\n",
    "Forecasting will be covered in detail in another notebook. \n",
    "But here we demonstrate the **Holt-Winters model**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VTlZe6uGnUGyZY8sL8WWOLS+kP/PMmfDSS/DII2GCs5dfhh12qGP0aBg6NHvFHnSGLyJtzOzZYfz8ZZfBOuvAyivDGWfAL34Rzuq7dq12wspRD19EMssdPvggfBr2gw9g1Ch46CHYcUe46CLo3r3aCZdOc3v4KvgikjmLFsENN8C550L79uG2guusEwr8gQdC587VTlgeFbkBipn1N7O3zGySmZ2eZ32NmT1kZuPMbKSZbZyzrj55foyZvVxqsHJJex8xn9gyx5YX4sscW16oXua33grz3Nx/f5jJ8qOPYNgw+Otf4dRTmy72Mb7PzVG04JtZO+BaoB/QAxhoZhs12uxMYIy7bwYcBlyds24xUOvum7t77/LEFhH5NvdQ1LffHn75Sxg+PJzZyxJFWzpmti0w2N13TZbPANzdL83Z5lHgYnf/T7L8DrCdu39mZpOBrdz9iyLHUUtHRFpk7tzwadiXXw5n9rH35ktViZZOZ+DDnOUpyXO5xgH7JAF6A12BLsk6B54ys1FmdmSpwUREipk/H/72t3AmP2tWuMVgWyn2LVGuYZmXAH82s1eB8cAYYFGyro+7TzOzNQiFf4K7P59vJ4MGDaJbt24A1NTU0KtXL2pra4ElvbXmLjc819LXV2O5cfZq58laXoCrrrqqLP++lLfw8tixYzn55JOb9fpttqnlscfgllvqGD8ellmmlpoa6NGjjt13h4MPXrL9zJlwySW1dOgAJ5xQx2abQceOS5e/4bk0vH+F8tXV1VFfX0+LuHuTD2Bb4F85y2cApxd5zWSgY57nBwOnFniNV8Lw4cMrst9Kii1zbHnd48scW1730jIvXuz+wAPu++/vXlvrXlPjvssu7tdc4z5unPt777mPHOl+yinuq6/ufu214TVjxrj36OH+29+G5dbMnCZJ3SxaxxsepfTwlwEmAn2BacDLwEB3n5CzzSrAbHdfkLRt+rj7IDPrALRz92/MbEVgGHCuuw/LcxwvlkVEsuPll8Mc81OmwOmnh9Ez3btDp075t3/3XdhzT+jQIbzm/PPDLQbbsrLPpePui8zseEKxbgfc7O4TzOzosNpvAroDt5nZYuAN4JfJy9cChpqZJ8e6K1+xF5G2Y/TocLeoKVPCMMljjoFlly3+uvXWgxdfDFMh7L03dOxY+ayZ05w/Byr5QC2d/4otc2x53ePLHFte9+9mnjfP/fe/d19zTfdbb3VfsKAqsZoU2/tMM1s6mktHRCrunXdg4ED4/vdh3LjwX2l9mlpBRCrqjjtC62bwYDjuOLCSO85SjObDF5GqmzIF/v53eOIJ+PJLePpp2GyzaqcSzYefQrFlji0vxJc5lrzucOedsMUWMHp0HWedBWPGxFPsY3mfW0pn+CJSFh9/HKY3mDgRnnwSvvoKks8NSUqohy8iS8U93Pv1lFPCuPizz4bll692qrZBPXwRaRXu4YYiF1wAkyfD44+HWwVKeqmHn0KxZY4tL8SXOU15Z8+GW26BrbeGgw4Kd4965ZXvFvs0ZS5VjJmbQ2f4IlISdxgyBH7zm1Dczz8f+vWDdpk/bcwO9fBFpKh33w0XZKdNgxtvhO22q3YigQrd4lBE2qb58+HCC2GbbWCXXcI8OCr28cp8wY+xJxdb5tjyQnyZK5n366/DZGT77AOXXBJuDfjmm+F2gb16hZuKvPIK/Pa3pU1y1hqZKyXGzM2hHr5IGzZvXij2664LffuGKYv/+Mdw4+8+fcIvgAEDNB1CVqiHL9JGzZgBhxwCK6wA994LyyxT7UTSXOrhi8h3uIc5bebODRdeH3ooTH+w7rpw110q9m1F5gt+jD252DLHlhfiy7w0eRcuhEGDoGtXqKmBTTeF66+HK66Aq6+u3KdiY3uPIc7MzaEevkiGzZ8Pv/gFzJwJn34abg8obZd6+CIZNXcu7L9/aNfce6/mt8ki9fBFhFmzwuiaDh3g/vtV7CXIfMGPsScXW+bY8kJ8mZuTd+ZM6N8fOneGu+9u3tj5cortPYY4MzdH5gu+SFvgHmasvPFG2H572GSTMMGZRt9ILvXwRSL04YdwxhlheuL27cMtBTt2hJ13hr32gv3204el2oLm9vBV8EUi4h6GVJ59drgh+MEHh2GXnTrBaqtVO520Nl20bSTGnlxsmWPLC/Flrqur4+uv4fDD4YYb4MUX4dxzYcMNoUePdBb72N5jiDNzc2S+4IvEzj3cI3ajjcLc8y++COuvX+1UEiO1dERSbPFiOPlkqKsLs1dus021E0ma6J62IhkxYQKcd164IDtiRJgWQWRpZL6lE2NPLrbMseWFdGd+/vklNxzp0iW0c8aOrat2rGZL83tcSIyZm6Okgm9m/c3sLTObZGan51lfY2YPmdk4MxtpZhuX+loRCa2bZ54JwykHDoSTToIPPoDLL9f8N1I+RXv4ZtYOmAT0BT4CRgEHuftbOdtcBnzt7ueb2YbAX9x9l1Jem7MP9fClzVm8GB58MLRuAI46KozE6dixurkkDpXo4fcG3nb395MDDAH2AnKL9sbAxQDuPtHMupnZGsB6JbxWpE2aMwcOPDDcXerSS2HXXfVhKamsUlo6nYEPc5anJM/lGgfsA2BmvYGuQJcSX1tRMfbkYsscW16ofubp06FfP1hppTDMcrfdmi721c7bEsqcPuW6aHsJsKqZvQocB4wBFpVp3yKZMm4cbL11eNxxR/UmN5O2p5SWzlTCGXuDLslz/+XuXwNHNCyb2WTgPaBDsdfmGjRoEN26dQOgpqaGXr16UVtbCyz5zdsWlmtra1OVJ2t5G9TV1bXq8UePhvHja3n0UTjmmDr69oV27dKbtxzLudnTkCf25Yav6+vraYlSLtouA0wkXHidBrwMDHT3CTnbrALMdvcFZnYk0MfdB5Xy2px96KKtZNb558Ott8KJJ4a+fadO1U4kWVD2uXTcfRFwPDAMeAMY4u4TzOxoMzsq2aw78LqZTQD6ASc19drmfENLq/GZRgxiyxxbXmjdzOecA0OGwAsvhE/NtqTY6z1uHTFmbo6SPmnr7v8CNmz03I05X49svL6p14q0FddcE4r9iBGw5prVTiNtnebSEamQBx4IZ/TPPw/JpSmRstJcOiIpMGIEHHssDBumYi/pobl0Uii2zLHlhcpmfu012H//cD/ZXr3Ks0+9x60jxszNkfmCL9KaHn4Y+vaFa68Nk5+JpIl6+CJlsHhxuAPVrbeGuXG23rraiaQtUA9fpJVNnw6HHRb+O2oUrLVWtROJ5Jf5lk6MPbnYMseWF8qT2R3uugs23hjWXRf+/e/KFfu2+h63thgzN4fO8EVaYNKkMArniy9C3163HpQYqIcv0gzz5sEll4QPVP3hD3DCCdBep01SJerhi1RAfX2Y2fKWW8JQyzFjYO21q51KpHnUw0+h2DLHlhdKy/zNN/D3v8NOO4VRN598AvfdB0OHtn6xz+p7nDYxZm4OneFLm/fVV+Hx+efwxhswfjy8/nqY7GyHHeD442GPPWD55audVGTpqIcvbca8eeHmI6NGwaefhiL/7LPw9tuw6qrh0aMHbLIJbLopbLutJjyTdGtuD18FXzJt0SK46abQe3/jDdhgg9Ce6dIFVlwxjK7ZbjtdeJU4lX0+/NjF2JOLLXNa8773HvTpE+a0ufzy0LIZOxb++lfYccc6TjsNtt8+jmKf1ve4KcqcPpkv+NI2vf8+7LxzuLvUs89CbS106FDtVCLVpZaOVM2cOeEG3uU+wx4zBvbbL8xFf8IJ5d23SJpoHL6k1oIF8OST8Nln4cLpPffAeuvBc8/B//zP0u9/9mw46yy480644go45JCl36dIlmS+pRNjTy62zKXmPe208OnUurpw0fS112CjjeDII8O8NC0xZw589FEYQrnVVuHr118vXuyz+h6niTKnj87wpVU8/DD84x+h3bLqqkue/+tfw4XV228PM042x4wZsMUW4cy+Qwe44AI4+ODy5hbJEvXwpeI+/TSMa3/44TAEsrF//QvOPBNGjwYrsRvpHor76quHm42ItEUahy+pc8IJoZBffXX+9YsXw/rrh+GTpcw6uWBBmLzs5pvhlVfK0/8XiZHG4TcSY08utsxN5X3nnXBx9qyzCr++XTs4+mi4/vqmj7NgAVx5JayzDjzyCNx/f8uLfZbe47RS5vRRD18q6g9/gFNOgTXWaHq7ww8PZ/lffgmrrbbk+UWL4IknYOLEMJFZp05hpM+mm1Y0tkgmqaUjFXPbbeFC6tixYRqDYo49NhT8e+4JLaCPPw6jbaZPD5+I3Wkn2HPP0vv8IlmnHr6kwsiRoTg/+yx0717aa+bOhR13hJ/+NPyC+POfw5DNwYPjmP5ApLWph99IjD252DLn5p01C847D3bfHW69tfRiD7DCCmGu+fvvDy2cYcPg/PMrU+xjfo9joczpo/MmKZtPPgnz12yySRg988MfNn8fP/hBKPYiUn4ltXTMrD9wFeEvgpvd/dJG61cG7gS6AssAV7j735N19cBXwGJggbv3LnAMtXQi9tFHsMsucNBBcPbZ1U4j0jaUvaVjZu2Aa4F+QA9goJlt1Giz44A33L0XsBNwhZk1/PWwGKh1980LFXuJ15w54cJsz57hk7Iq9iLpVUoPvzfwtru/7+4LgCHAXo22cWCl5OuVgC/cfWGybCUepyJi7MnFkvnLL8NZ/RNP1PHSS3D66dVOVLpY3uMGseUFZU6jUgpxZ+DDnOUpyXO5rgU2NrOPgHHASTnrHHjKzEaZ2ZFLE1bS48MPw1DJ7bYLF1bXW6/aiUSkmKI9fDPbF+jn7kcly4cAvd39xEbb/Njdf2Nm6wFPAT3d/Rsz6+Tu08xsjeT54939+TzHUQ8/Em++CbvuGqZMOO20aqcRabsqMR/+VMLF2AZdkudyHQ5cDODu75rZZGAj4BV3n5Y8/5mZDSW0iL5T8AEGDRpEt27dAKipqaFXr17U1tYCS/7U0nJ1l7t2rWXnneGII+rYaiuAdOXTspazvNzwdX19PS3i7k0+CKNu3gHWAZYDxgLdG23zF2Bw8vVahBbQakAHoGPy/IrAf4CfFTiOV8Lw4cMrst9KSmvmhQvd+/Rxv+KKbz+f1rxNiS1zbHndlbk1JHWzaB1veBQ9w3f3RWZ2PDCMJcMyJ5jZ0cnBbgIuAP5uZq8lL/udu39pZj8EhpqZE/6auMvdh7XsV5NU26WXwvLLh1sHikh8NLWClOT228MonJdfhrXXrnYaEQHd01Yq4C9/CWf3zzyjYi8SM82lk0Jpyvy3v8Hll8OIEYXnxUlT3lLFljm2vKDMaaQzfMlr3rww+dl554UZL5PBUyISMfXw5VtmzoQLLwy3D+zZM0xRrJuNiKSTpkeWFlm8ONxRasMN4bPPwg3Fn3lGxV4kSzJf8GPsybV25gkToE+fcE/Zf/wDbrkl3De2VHqPKy+2vKDMaZT5gi9Ne/hh2GEHGDQIXnwRems+U5HMUg+/jfr88zCu/umn4YEHYOutq51IRJpLPXwp6qWXwl2pVl4Zxo9XsRdpKzJf8GPsyVUy8wsvwIABYXz9lVeGor+09B5XXmx5QZnTSOPw25DnnoN99w3TJPTvX+00ItLa1MNvI4YPhwMOgHvuCXepEpH4qYcv3/Hgg3DggXD//Sr2Im1Z5gt+jD25cma+6SY48UR48klI7qVQdm39PW4NseUFZU6jzBf8rJs/PxT1kSPDp2Vz3X13mAtnxAjYfPPq5BOR9FAPP2Lz5oW+/Oefw1dfhf/usUeYDuHNN8OnZp9+OgzBFJHsUQ+/jZg7N4y4ad8e6urg9dfDkMtNN4WJE0ORf+45FXsRWSLzBT/GnlyxzHPnws9/Dh06wJAhsOyy4fl114WTToLrroMTToD11698Vsjme5w2seUFZU6jzBf8rPniC9h9d1hlldCjbyj2IiLFqIcfkddeC2f2++4LF10U2jki0nbpnrYZ9cADcMwx4YYkBx9c7TQiEqPMt3Ri7Mk1zvynP8Fpp4Wx9Gks9ll4j9MutrygzGmkM/yUu+suuOrNCuohAAAM2klEQVSqMAKnS5dqpxGRmKmHn2JDh8Kvfx1uNdijR7XTiEjaqIefATNmwOWXwx13wKOPqtiLSHmoh58i33wTJjnr3LmOd96BUaPiuDlJTO9xg9gyx5YXlDmNdIafEgsWwP77w/e/D/feG6ZIEBEpJ/Xwq+Tf/4b6eujVC6ZNC3egWrAgzH+j8fUiUoqKzKVjZv3N7C0zm2Rmp+dZv7KZPWJmY81svJkNKvW1bc38+XDqqXDEEWEOnEGDwiicLbeE++5TsReRyila8M2sHXAt0A/oAQw0s40abXYc8Ia79wJ2Aq4ws/Ylvrai0taTO/fccOPwMWPCRdnx48OMlmedBSuuGLZJW+ZiYssL8WWOLS8ocxqVcj7ZG3jb3d8HMLMhwF7AWznbOLBS8vVKwBfuvtDMti3htW3GjBlwww0wejSstlq104hIW1O0h29m+wL93P2oZPkQoLe7n5izTUfgEWAjoCNwoLs/Ucprc/aR+R7+hRfCpElw223VTiIiWVCtcfj9gDHuvrOZrQc8ZWY9y7TvTJg1C66+OvTtRUSqoZSCPxXomrPcJXku1+HAxQDu/q6ZTSac7Zfy2v8aNGgQ3bp1A6CmpoZevXpRm9yItaG31tzlhuda+vpyLM+fD7vtVkfPntC9e/HtG2dv7bzNXY4tL8BVV11Vln9fylt4eezYsZx88smpyVPKcsNzacmTL19dXR319fW0iLs3+QCWAd4B1gGWA8YC3Rtt8xdgcPL1WsCHwGqlvDZnH14Jw4cPr8h+SzVnjvtuu7nvvbf73LmlvabamZsrtrzu8WWOLa+7MreGpG4WreMNj5LG4ZtZf+DPhFE9N7v7JWZ2dHKwm8ysE/B3oFPykovd/Z5Cry1wDC8lS0wWLoT99oPllguToOlmJSJSTs3t4euDVxUwa1a4x+yf/wzTp4cPUy23XLVTiUjW6CbmjeT2virJPRT2PfcM0yMcc0y4DeGDDza/2LdW5nKJLS/Elzm2vKDMaaTPdZbBpEmhwH/2WbhRyZ13wsorVzuViMi3qaWzlCZMgJ12gtNPhxNO0NQIItJ6NB9+K5o8Gfr1g8sug0MPrXYaEZGmqYffQi+8AH36wO9/X/5iH1sfMba8EF/m2PKCMqeRzvCb6bXXwlTGQ4aEKRJ23bXaiURESqMefglmz4azz4aHHw7TGx9+OPzqV7D22tVOJiJtmXr4Zfb55zBgAKy7bhhi2bMnWMlvr4hIeqiH34Rp02D77cMonDvvhM02a51iH1sfMba8EF/m2PKCMqdR5gt+S338cSj0//u/cNFFOqsXkfiph5/HmDHhhuKHHRbuRCUikkbq4bfQjBnw/PPwn/+EUTjXXAMHHVTtVCIi5ZP5lk6xntzkyeFMvlu3MNkZhKJfzWIfWx8xtrwQX+bY8oIyp1GbPsMfMQIOOACOPRbefhvWWKPaiUREKqfN9vBvuQXOOCOMvvnZz1rtsCIiZaMefhHz58OZZ4apjEeMgI02qnYiEZHW0aZ6+P/+dxhL/9ZbMHJkeot9bH3E2PJCfJljywvKnEZt4gx/zhz47W/hn/8Mo28GDNC4ehFpezLfw3/qKTj+eNhyS7juOqipKfshRESqQj38xIIFcPTRUFcXhlsOGFDtRCIi1ZXJHv7s2bD33uGWg9ddVxddsY+tjxhbXogvc2x5QZnTKFMFv+FG4r16hTH1Dz0EK6xQ7VQiIukQdQ9/+nS4/XZ4991wI/HRo6FTp3DLwf79KxRURCQlmtvDj7LgL14MN9wA554b7im7xRZhvvqttoIf/KDCQUVEUqK5BT+6ls7770PfvnD33fD00+EM/+STYc898xf7GHtysWWOLS/Elzm2vKDMaRRVwX/8cdh663BW/+yzsOmm1U4kIhKPVLV05s1z7rkHJkwIwyh79oSZM8ONwx97DIYOhfvugz59qp1WRKT6oh6H/6MfwQYbwDbbwK9/De+9B6usAhtuCDvuCK+8Ei7KiohI85XU0jGz/mb2lplNMrPT86w/zczGmNmrZjbezBaaWU2yrt7MxiXrX27qONdfH/ryF14I48fDrFnw0UcwfDicc07Lin2MPbnYMseWF+LLHFteUOY0KlrwzawdcC3QD+gBDDSzb0075u7/5+6bu/sWwO+BOnefkaxeDNQm63s3dazdd2/Jt9C0sWPHln+nFRZb5tjyQnyZY8sLypxGpZzh9wbedvf33X0BMATYq4ntBwL35CxbicepiBkzZhTfKGViyxxbXogvc2x5QZnTqJRC3Bn4MGd5SvLcd5jZ/wD9gQdznnbgKTMbZWZHtjSoiIgsnXJftB0APJ/TzgHo4+7TzGwNQuGf4O7Pl/m4BdXX17fWocomtsyx5YX4MseWF5Q5jYoOyzSzbYFz3L1/snwG4O5+aZ5tHwLuc/chBfY1GPja3f+UZ106xoeKiESkrFMrmNkywESgLzANeBkY6O4TGm23CvAe0MXd5yTPdQDaufs3ZrYiMAw4192HNeP7ERGRMija0nH3RWZ2PKFYtwNudvcJZnZ0WO03JZvuDTzZUOwTawFDk7P39sBdKvYiItWRmk/aiohIZUU1l46IiLScCr6ISBuhgi8i0kZkvuCb2TPVzlCImf3czFZLvl7DzG5P5iK618y6VDtfPma2mpmdbWa/suAPZvaomV1uZqtWO18hZtbPzK43s0eSx/Vmltr7oiV5f2lm3Ro9f0R1EjWffvbKqxw/e5m6aGtmrzV+CtiAMKwUd+/Z6qGaYGZvuvvGydf3AiOB+4FdgF+4+0+rmS8fM3scGA+sDHRPvr4P+Cmwmbs3Ne1GVZjZVYR/B7cTPikO0AU4lDBtyEnVypaPmV0E/AR4lfBhxqvc/Zpk3avJnFWpop+9yivHz17WCv4jwEzgAmAO4R/dc4QfHtz9/eql+y4zm+juGyZfj3b3LXPWjXX3XtVLl19DLjMzYIq7d268rorx8jKzSe6+QZ7nDZjk7utXIVZBZjYe2NzdG2advRuY6O6nmNkYd9+8yhG/Qz97lVeOn71MtXTcfU/CPD43EX7j1QMLkonfUvUPLlFnZuclcxDVmdnPAcxsJ+Cr6kYrqF3y5+PaQMeGloOZrQ4sV8VcTZlrZlvneX5rYG5rhylBe3dfCJBMUzIAWNnM7iel77F+9lrF0v/suXvmHsCKwJ+AfxB+E1Y9U4GcywLnAB8kj8XA14Qzuq7Vzlcg80Dgk+SxL/A08BQwFTiq2vkKZN4CeAl4k/ABwmHABMKf8VtWO1+evI8CO+Z5/gJgcbXzFcmun73KZV7qn71MtXQaM7PNgO3c/YZqZykmmZqivbt/Ue0sxSTTbZiHlkN7oBcw1d2nVTlak8zs+yyZ6XWqu39czTyFJGed+Lc/td6wrrO7T239VM2jn73KWNqfvcwV/KS/1ZucH2zgZU/pNxpbXogzcyFmtpG7v1XtHKWKLS/Elzm2vFB65kwVfDP7GXAd8DahCEEYjfEj4FhP2Tw+seWFODM3xcw+cPeu1c5RqtjyQnyZY8sLpWdO1U3My+DPwC4eLhj9l5n9EHicMJQpTWLLCxFmNrOrC60CalozSyliywvxZY4tL5Qnc9YKfnuWjLPONZVwkSZtYssLcWY+HPgNMC/PuoGtnKUUseWF+DLHlhfKkDlrBf8WYJSZDWHJbRnXBg4Cbq5aqsJiywtxZh4FvO7uLzReYWbntH6comLLC/Flji0vlCFzpnr4AGa2MbAn376g+Ii7v1m9VIXFlhfiy5x8hH6uu8+udpZSxJYX4sscW14oT+bMFXwREckvU5+0NbNVzOwSM3vLzL40sy/MbELyXOouxMSWF5S5NcSWF+LLHFteKE/mTBV8wkRC04Fad1/N3VcHdkqeu6+qyfKLLS8oc2uILS/Elzm2vFCGzJlq6VjOhEjNWVctseUFZW4NseWF+DLHlhfKkzlrZ/jvm9nvzGythifMbC0zO50lI0rSJLa8oMytIba8EF/m2PJCGTJnreAfCKwOPGtm083sS6AOWA04oJrBCogtLyhza4gtL8SXOba8UIbMmWrpQJhTgvBR/5Hu/k3O8/3d/V/VS5ZfbHlBmVtDbHkhvsyx5YUyZC7X1J1peAAnEu6w8zBQD+yVs+7VaueLPa8yK29WMseWt1yZq/5NlPkNGQ90TL7uBrwCnJQsj6l2vtjzKrPyZiVzbHnLlTlrUyu08+TPHHevN7Na4AEzW4cwwVDaxJYXlLk1xJYX4sscW14oQ+asXbT9xMz+e1/H5M3ZA/gesGnVUhUWW15Q5tYQW16IL3NseaEMmTN10dbMugALPc+djMysj7v/pwqxCootLyhza4gtL8SXOba8UJ7MmSr4IiJSWNZaOiIiUoAKvohIG6GCLyLSRqjgi4i0ESr4IiJtxP8D+8C0OFobEOgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7b9a62c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Visualize unified inflation levels:\n",
    "plot(infl[start:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='holt-winters-equations.png', embed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can safely ignore the seasonal portion of the model \n",
    "since the relevant data has been deseasonalized at the upstream source.\n",
    "\n",
    "Two important parameters, $\\alpha$ and $\\beta$, must estimated. \n",
    "We developed a robust L1 estimation technique which \n",
    "minimizes one-step ahead forecast errors, \n",
    "conditional on specific data. See \n",
    "[ys_opt_holt module](https://github.com/rsvp/fecon235/blob/master/lib/ys_opt_holt.py) \n",
    "for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " !.  WARNING: ipykernel_launcher.py Optimizing Holt-Winters alphabetaloss may take TIME!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0, 0.3673, 0.0307, 0.00030739713869681884]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  This optimization procedure may be computationally intense, \n",
    "#  depending on data size and the number of grids.\n",
    "\n",
    "ab = optimize_holt( infl, grids=50 )\n",
    "\n",
    "ab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first two elements in the `ab` list are $\\alpha$ and $\\beta$ respectively. \n",
    "The third element gives the median absolute loss as percent for an \n",
    "one-step ahead forecast given those parameters. \n",
    "The fourth element is the median absolute loss \n",
    "(due to our L1 loss function for robust optimization).\n",
    "\n",
    "The median absolute loss is 0.00031 \n",
    "which gives us confidence that the model has performed well.\n",
    "\n",
    "Using the estimated parameters, we can now proceed to \n",
    "generate our 12-step ahead forecast. See \n",
    "[yi_timeseries module](https://github.com/rsvp/fecon235/blob/master/lib/yi_timeseries.py) \n",
    "for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Forecast\n",
       "0   1.000000\n",
       "1   1.002566\n",
       "2   1.005132\n",
       "3   1.007698\n",
       "4   1.010264\n",
       "5   1.012829\n",
       "6   1.015395\n",
       "7   1.017961\n",
       "8   1.020527\n",
       "9   1.023093\n",
       "10  1.025659\n",
       "11  1.028225\n",
       "12  1.030791"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holtdf = holt( infl, alpha=ab[0], beta=ab[1] )\n",
    "holtforecast( holtdf, h=12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the most current level of unified inflation \n",
    "is 1.00 by construction, it is easy to discern that: \n",
    "***the 12-months ahead forecast implies an annualized inflation rate of 3.08%.***\n",
    "\n",
    "From the standpoint of Fed policy, this upward acceleration \n",
    "of the inflation rate is a bit alarming \n",
    "since the move is greater than two standard deviations. \n",
    "It would favor more increased hikes in the Fed funds rate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting summary\n",
    "\n",
    "Forecasting is an art. \n",
    "A pearl of wisdom is to combine orthogonal methods \n",
    "to arrive at excellence.\n",
    "\n",
    "Here are three non-related ways which characterized inflation:\n",
    "\n",
    "- BEI, Break-even Inflation: a long-term forecast implied by the bond market\n",
    "- Geometric mean rate: internal growth rate of a stochastic trend\n",
    "- Holt-Winters model: projection obtained by robust prediction error minimization\n",
    "\n",
    "The respective results are simply averaged..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.33"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "( 2.04 + 1.87 + 3.08 ) / 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many sorts of inflation measures, \n",
    "but we devised a mathematically unified version. \n",
    "The bond market has its own version, \n",
    "so for the **one-year ahead forecast**, \n",
    "all in all, we shall settle for:\n",
    "**2.33%**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Appendix 1:  Overall Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Conversions to dataframe with label before paste:\n",
    "dfa = todf( inf_av, 'Iav')\n",
    "dfb = todf( bei, 'BEI' )\n",
    "dfc = todf( bei_inf_av, 'BEI_Iav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Mother of all annualized inflation rates:\n",
    "infall = paste( [inf, dfa, dfb, dfc] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "              CPI      CPIc       PCE      PCEc       Iav       BEI   BEI_Iav\n",
       "CPI      1.000000  0.396734  0.991881  0.727324  0.973333  0.596251  0.930337\n",
       "CPIc     0.396734  1.000000  0.380431  0.812429  0.578716  0.157139  0.473951\n",
       "PCE      0.991881  0.380431  1.000000  0.759407  0.974031  0.624653  0.942230\n",
       "PCEc     0.727324  0.812429  0.759407  1.000000  0.862696  0.493943  0.810729\n",
       "Iav      0.973333  0.578716  0.974031  0.862696  1.000000  0.587671  0.945828\n",
       "BEI      0.596251  0.157139  0.624653  0.493943  0.587671  1.000000  0.818525\n",
       "BEI_Iav  0.930337  0.473951  0.942230  0.810729  0.945828  0.818525  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  CORRELATION matrix going back to 2003-01-01 (start of BEI):\n",
    "cormatrix(infall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CPI and PCE are tightly correlated, as are CPIc and PCEc. \n",
    "Surprisingly, CPI and CPIc are only moderately correlated, only 40%. \n",
    "Recall that headline inflation includes food and energy, \n",
    "whereas core inflation excludes those components.\n",
    "\n",
    "The market traded BEI break-even inflation is modestly correlated \n",
    "with government-released statistics of inflation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most recent computed data\n",
    "\n",
    "Given in annualized percentage form:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                 CPI      CPIc       PCE      PCEc       Iav   BEI   BEI_Iav\n",
       "T                                                                           \n",
       "2017-02-01  2.765943  2.197494  2.176721  1.861760  2.250479  2.02  2.135240\n",
       "2017-03-01  2.392636  1.991838  1.825633  1.610806  1.955228  1.99  1.972614\n",
       "2017-04-01  2.195585  1.881641  1.732041  1.577605  1.846718  1.91  1.878359\n",
       "2017-05-01  1.867699  1.740672  1.514083  1.475945  1.649600  1.83  1.739800\n",
       "2017-06-01  1.648658  1.719232  1.416262  1.504955  1.572277  1.73  1.651138\n",
       "2017-07-01  1.740413  1.710167  1.404816  1.412455  1.566963  1.77  1.668481\n",
       "2017-08-01  1.950792  1.694847  1.435583  1.298376  1.594899  1.78  1.687450\n",
       "2017-09-01  2.230133  1.699529  1.655954  1.351617  1.734308  1.83  1.782154\n",
       "2017-10-01  2.034204  1.774644  1.593239  1.468763  1.717713  1.86  1.788856\n",
       "2017-11-01  2.201742  1.712094  1.742919  1.505658  1.790604  1.85  1.820302\n",
       "2017-12-01  2.111342  1.760828  1.705745  1.526629  1.776136  1.90  1.838068\n",
       "2018-01-01  2.137869  1.845520  1.650447  1.515219  1.787264  2.04  1.913632"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Inflation RATES\n",
    "tail(infall, n=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix 2: Forecasting process as a function\n",
    "\n",
    "We can think of this notebook as the documentation \n",
    "which encapsulates its findings and process \n",
    "into a single function: `foreinfl()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mforeinfl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3673\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mSource:\u001b[0m   \n",
       "\u001b[0;32mdef\u001b[0m \u001b[0mforeinfl\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.3673\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m'''Forecast Unified Inflation 1-year ahead per fred-inflation.ipynb.'''\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#  Holt-Winters parameters alpha and beta are optimized\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#  from the 1960-2018 dataset, consisting of 697 monthly points.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#  Each \"way\" is an orthogonal method, to be averaged as way[0].\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mway\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minflall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mm4infl\u001b[0m \u001b[0;34m)\u001b[0m  \u001b[0;31m# synthetic Unified Inflation, monthly.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minfl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minflall\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#                    ^Default n=120 months, i.e. last 10 years.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" 00:00:00\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#                ^Most recent month for CPI, CPIc, PCE, PCEc data.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgemrat\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0minfl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myearly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m12\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgm\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m#  Geometric Mean Rate over n months.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mhw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforeholt\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0minfl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m \u001b[0;34m)\u001b[0m  \u001b[0;31m# Holt-Winters model.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtailvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m   \u001b[0;31m# Convert forecasted level to rate.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbond10\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm4bond10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtips10\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm4tips10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbei\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtodf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbond10\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mtips10\u001b[0m\u001b[0;34m)\u001b[0m   \u001b[0;31m#  10-year BEI Break-even Inflation.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#         ^Bond market data will be more recent than m4infl.\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtailvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbei\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#        Final forecast is the AVERAGE of orthogonal ways:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mway\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#     \"way\" in SUMMARY is thus: [Average, \"infl-date\", GMR, HW, BEI]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m#                e.g. [2.2528, '2018-01-01', 1.5793, 3.0791, 2.1000]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0mway\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Dropbox/ipy/fecon235/fecon235.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Query into its details:\n",
    "foreinfl??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.252780993104452,\n",
       " '2018-01-01',\n",
       " 1.5792838373316131,\n",
       " 3.0790591419817437,\n",
       " 2.0999999999999996]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  See it in action:\n",
    "foreinfl()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are slight variations from the notebook derivation.\n",
    "\n",
    "- Instead of `start` we use a rolling window of `n` monthly datapoints.\n",
    "- By default, `n` is set to 120, i.e. the last ten years.\n",
    "- Given new data, we do not re-optimize the Holt-Winters parameters, since that would be computationally expensive. The default values for alpha and beta are battle-tested using data dating back to 1960.\n",
    "- Bond market data does not suffer from release lag, unlike inflation statistics, so the most *current* BEI is used in the `foreinfl()` function.\n",
    "\n",
    "To obtain an inflation forecast in practice, \n",
    "executing a single Python function is far more convenient \n",
    "than re-running a Jupyter notebook. \n",
    "The list output also gives a summary from the orthogonal methods \n",
    "which were utilized."
   ]
  }
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